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Record W3006619411

Cellulose nanocrystal (CNC)–inorganic hybrid systems: synthesis, properties and applications

2018· article· en· W3006619411 on OpenAlexaff
Muhammad Shahidul Islam, Lei Chen, Jared Sisler, Kam Chiu Tam

Bibliographic record

VenueJournal of Materials Chemistry · 2018
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsNanomaterialsMaterials scienceSurface modificationNanotechnologyNanocrystalCelluloseDrug deliveryChemical engineering
DOInot available

Abstract

fetched live from OpenAlex

Cellulose nanocrystal (CNC), a class of sustainable nanomaterial derived from forest and agro-biomass can serve as nature's storage for carbon dioxide. It has many attractive features, such as large specific surface area, high tensile strength and stiffness, abundance of surface hydroxyl groups, and they are also biocompatible, biodegradable and renewable. When dispersed in a polar solvent, they assemble to form multiphase or higher order structures yielding desirable optical and structural properties. They are being explored as templates for the design of a wide range of new functional nanomaterials. CNCs are excellent support for the loading of inorganic nanoparticles (e.g. Ag, Au, Pt, Pd etc.) yielding stable nano-hybrids in aqueous media. Additional surface functionalization of CNCs impart new and attractive physicochemical properties that are being exploited for application in sensors, catalysts, drug delivery vehicles, anti-microbial agents, scaffold for tissue engineering, biomarkers etc. This review provides an overview and future perspective on recent advances in the development on functional CNC–inorganic hybrids with potential applications in biomedical and chemical systems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.585

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.244
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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